[Feature] PD-Multiplexing Context and Scheduler, lazy import spatial. (#12275)

python/sglang/srt/layers/logits_processor.py

@@ -134,10 +134,7 @@ class LogitsMetadata:
     @classmethod
     def from_forward_batch(cls, forward_batch: ForwardBatch):
         if (
-            (
             forward_batch.forward_mode.is_extend()
-                or forward_batch.forward_mode.is_split_prefill()
-            )
             and forward_batch.return_logprob
             and not forward_batch.forward_mode.is_target_verify()
         ):
@@ -384,8 +381,8 @@ class LogitsProcessor(nn.Module):
             input_logprob_indices = None
         elif (
             logits_metadata.forward_mode.is_extend()
-            or logits_metadata.forward_mode.is_split_prefill()
-        ) and not logits_metadata.extend_return_logprob:
+            and not logits_metadata.extend_return_logprob
+        ):
             # Prefill without input logprobs.
             if logits_metadata.padded_static_len < 0:
                 last_index = torch.cumsum(logits_metadata.extend_seq_lens, dim=0) - 1

python/sglang/srt/managers/schedule_batch.py

@@ -72,7 +72,11 @@ from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
 from sglang.srt.mem_cache.radix_cache import RadixKey
 from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
 from sglang.srt.metrics.collector import SchedulerMetricsCollector, TimeStats
-from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode, ForwardMode
+from sglang.srt.model_executor.forward_batch_info import (
+    CaptureHiddenMode,
+    ForwardBatch,
+    ForwardMode,
+)
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.sampling.sampling_params import SamplingParams
 from sglang.srt.server_args import ServerArgs, get_global_server_args

python/sglang/srt/managers/scheduler.py

@@ -152,6 +152,7 @@ from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
 from sglang.srt.mem_cache.mamba_radix_cache import MambaRadixCache
 from sglang.srt.mem_cache.radix_cache import RadixCache
 from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
+from sglang.srt.multiplex.multiplexing_mixin import SchedulerMultiplexMixin
 from sglang.srt.parser.reasoning_parser import ReasoningParser
 from sglang.srt.server_args import PortArgs, ServerArgs, get_global_server_args
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
@@ -213,6 +214,7 @@ class Scheduler(
     SchedulerMetricsMixin,
     SchedulerDisaggregationDecodeMixin,
     SchedulerDisaggregationPrefillMixin,
+    SchedulerMultiplexMixin,
     SchedulerRuntimeCheckerMixin,
     SchedulerPPMixin,
 ):
@@ -252,6 +254,7 @@ class Scheduler(
         self.enable_lora = server_args.enable_lora
         self.max_loras_per_batch = server_args.max_loras_per_batch
         self.enable_overlap = not server_args.disable_overlap_schedule
+        self.enable_pdmux = server_args.enable_pdmux
         self.skip_tokenizer_init = server_args.skip_tokenizer_init
         self.enable_metrics = server_args.enable_metrics
         self.enable_metrics_for_all_schedulers = (
@@ -285,6 +288,10 @@ class Scheduler(
         # Init inter-process communication
         self.init_sockets(server_args, port_args)
 
+        # Init pdmux context
+        if self.enable_pdmux:
+            self.init_pdmux()
+
         # Init tokenizer
         self.init_tokenizer()
 
@@ -424,6 +431,8 @@ class Scheduler(
         self.running_batch: ScheduleBatch = ScheduleBatch(reqs=[], batch_is_full=False)
         # The current forward batch
         self.cur_batch: Optional[ScheduleBatch] = None
+        # The current split prefill batch
+        self.split_prefill_batch: Optional[ScheduleBatch] = None
         # The last forward batch
         self.last_batch: Optional[ScheduleBatch] = None
         self.forward_ct = 0
@@ -1952,7 +1961,6 @@ class Scheduler(
 
         # Run forward
         if self.is_generation:
-
             batch_or_worker_batch = batch
 
             if self.enable_overlap or self.spec_algorithm.is_none():
@@ -2009,6 +2017,9 @@ class Scheduler(
                     # The future value, usually for next batch preparation
                     # Current implementation strictly synchronizes the seq_lens
                     batch.seq_lens = batch_result.next_draft_input.new_seq_lens
+            elif self.enable_pdmux and batch.forward_mode.is_split_prefill():
+                batch_result = self.tp_worker.forward_batch_split_prefill(batch)
+                future_indices_or_next_token_ids = batch_result.next_token_ids
             else:
                 batch_result = self.model_worker.forward_batch_generation(
                     batch_or_worker_batch
@@ -2791,7 +2802,9 @@ def run_scheduler_process(
 
         disaggregation_mode: DisaggregationMode = scheduler.disaggregation_mode
         if disaggregation_mode == DisaggregationMode.NULL:
-            if server_args.pp_size > 1:
+            if scheduler.enable_pdmux:
+                scheduler.event_loop_pdmux()
+            elif server_args.pp_size > 1:
                 scheduler.event_loop_pp()
             elif scheduler.enable_overlap:
                 scheduler.event_loop_overlap()

python/sglang/srt/managers/tp_worker.py

@@ -35,7 +35,7 @@ from sglang.srt.managers.io_struct import (
     UpdateWeightsFromIPCReqInput,
     UpdateWeightsFromTensorReqInput,
 )
-from sglang.srt.managers.schedule_batch import ModelWorkerBatch
+from sglang.srt.managers.schedule_batch import ModelWorkerBatch, ScheduleBatch
 from sglang.srt.managers.scheduler import GenerationBatchResult
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
@@ -425,3 +425,26 @@ class TpModelWorker(BaseTpWorker):
                 pp_hidden_states_proxy_tensors=pp_proxy_tensors,
                 can_run_cuda_graph=can_run_cuda_graph,
             )
+
+    def forward_batch_split_prefill(self, batch: ScheduleBatch):
+        if batch.split_index == 0:
+            model_worker_batch = batch.get_model_worker_batch()
+            forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
+            batch.split_forward_batch = forward_batch
+            batch.seq_lens_cpu_cache = model_worker_batch.seq_lens_cpu
+        else:
+            model_worker_batch = batch.get_model_worker_batch(batch.seq_lens_cpu_cache)
+
+        logits_output, can_run_cuda_graph = self.model_runner.forward(
+            batch.split_forward_batch, split_forward_count=batch.split_forward_count
+        )
+        if logits_output:
+            next_token_ids = self.model_runner.sample(logits_output, model_worker_batch)
+        else:
+            next_token_ids = None
+        batch_result = GenerationBatchResult(
+            logits_output=logits_output,
+            can_run_cuda_graph=can_run_cuda_graph,
+        )
+        batch_result.next_token_ids = next_token_ids
+        return batch_result

python/sglang/srt/mem_cache/memory_pool.py

@@ -509,6 +509,7 @@ class MHATokenToKVPool(KVCache):
         enable_memory_saver: bool,
         start_layer: Optional[int] = None,
         end_layer: Optional[int] = None,
+        enable_alt_stream: bool = True,
         enable_kv_cache_copy: bool = False,
     ):
         super().__init__(
@@ -527,7 +528,9 @@ class MHATokenToKVPool(KVCache):
         self._create_buffers()
 
         self.device_module = torch.get_device_module(self.device)
-        self.alt_stream = self.device_module.Stream() if _is_cuda else None
+        self.alt_stream = (
+            self.device_module.Stream() if _is_cuda and enable_alt_stream else None
+        )
 
         if enable_kv_cache_copy:
             self._init_kv_copy_and_warmup()

python/sglang/srt/model_executor/forward_batch_info.py

@@ -96,6 +96,7 @@ class ForwardMode(IntEnum):
                 else False
             )
             or self == ForwardMode.TARGET_VERIFY
+            or self == ForwardMode.SPLIT_PREFILL
         )
 
     def is_decode(self):

python/sglang/srt/model_executor/model_runner.py

@@ -1765,6 +1765,7 @@ class ModelRunner:
                     enable_memory_saver=self.server_args.enable_memory_saver,
                     start_layer=self.start_layer,
                     end_layer=self.end_layer,
+                    enable_alt_stream=not self.server_args.enable_pdmux,
                     enable_kv_cache_copy=(
                         self.server_args.speculative_algorithm is not None
                     ),
@@ -1833,12 +1834,18 @@ class ModelRunner:
 
     def init_attention_backend(self):
         """Init attention kernel backend."""
-        if self.server_args.enable_two_batch_overlap and not self.is_draft_worker:
+        if self.server_args.enable_pdmux:
+            self.attn_backend = self._get_attention_backend(init_new_workspace=True)
+            self.decode_attn_backend_group = []
+            for _ in range(self.server_args.sm_group_num):
+                self.decode_attn_backend_group.append(self._get_attention_backend())
+            self.decode_attn_backend = self.decode_attn_backend_group[0]
+        elif self.server_args.enable_two_batch_overlap and not self.is_draft_worker:
             self.attn_backend = TboAttnBackend.init_new(self._get_attention_backend)
         else:
             self.attn_backend = self._get_attention_backend()
 
-    def _get_attention_backend(self):
+    def _get_attention_backend(self, init_new_workspace: bool = False):
         """Init attention kernel backend."""
         self.prefill_attention_backend_str, self.decode_attention_backend_str = (
             self.server_args.get_attention_backends()
@@ -1852,10 +1859,12 @@ class ModelRunner:
             attn_backend = HybridAttnBackend(
                 self,
                 decode_backend=self._get_attention_backend_from_str(
-                    self.decode_attention_backend_str
+                    self.decode_attention_backend_str,
+                    init_new_workspace=init_new_workspace,
                 ),
                 prefill_backend=self._get_attention_backend_from_str(
-                    self.prefill_attention_backend_str
+                    self.prefill_attention_backend_str,
+                    init_new_workspace=init_new_workspace,
                 ),
             )
             logger.info(
@@ -1869,7 +1878,8 @@ class ModelRunner:
             )
         else:
             attn_backend = self._get_attention_backend_from_str(
-                self.server_args.attention_backend
+                self.server_args.attention_backend,
+                init_new_workspace=init_new_workspace,
             )
 
         (
@@ -1878,9 +1888,12 @@ class ModelRunner:
         ) = (self.prefill_attention_backend_str, self.decode_attention_backend_str)
         return attn_backend
 
-    def _get_attention_backend_from_str(self, backend_str: str):
+    def _get_attention_backend_from_str(
+        self, backend_str: str, init_new_workspace: bool = False
+    ):
         if backend_str not in ATTENTION_BACKENDS:
             raise ValueError(f"Invalid attention backend: {backend_str}")
+        self.init_new_workspace = init_new_workspace
         full_attention_backend = ATTENTION_BACKENDS[backend_str](self)
         return attn_backend_wrapper(self, full_attention_backend)
 
@@ -1978,6 +1991,9 @@ class ModelRunner:
         device_mesh = torch.distributed.init_device_mesh(self.device, (self.tp_size,))
         tensor_parallel(self.model, device_mesh)
 
+    def update_decode_attn_backend(self, stream_idx: int):
+        self.decode_attn_backend = self.decode_attn_backend_group[stream_idx]
+
     def forward_decode(
         self,
         forward_batch: ForwardBatch,
@@ -1985,6 +2001,10 @@ class ModelRunner:
         pp_proxy_tensors=None,
     ) -> LogitsProcessorOutput:
         if not skip_attn_backend_init:
+            if self.server_args.enable_pdmux:
+                self.decode_attn_backend.init_forward_metadata(forward_batch)
+                forward_batch.attn_backend = self.decode_attn_backend
+            else:
                 self.attn_backend.init_forward_metadata(forward_batch)
         # FIXME: add pp_proxy_tensors arg to all models
         kwargs = {}
@@ -2123,18 +2143,18 @@ class ModelRunner:
                 skip_attn_backend_init=skip_attn_backend_init,
                 pp_proxy_tensors=pp_proxy_tensors,
             )
-        elif forward_batch.forward_mode.is_extend():
-            ret = self.forward_extend(
-                forward_batch,
-                skip_attn_backend_init=skip_attn_backend_init,
-                pp_proxy_tensors=pp_proxy_tensors,
-            )
         elif forward_batch.forward_mode.is_split_prefill():
             ret = self.forward_split_prefill(
                 forward_batch,
                 reinit_attn_backend=reinit_attn_backend,
                 forward_count=split_forward_count,
             )
+        elif forward_batch.forward_mode.is_extend():
+            ret = self.forward_extend(
+                forward_batch,
+                skip_attn_backend_init=skip_attn_backend_init,
+                pp_proxy_tensors=pp_proxy_tensors,
+            )
         elif forward_batch.forward_mode.is_idle():
             ret = self.forward_idle(forward_batch, pp_proxy_tensors=pp_proxy_tensors)
         else:

python/sglang/srt/multiplex/multiplexing_mixin.py

+"""
+Mixin class providing multiplexing scheduling logic
+"""
+
+import logging
+
+import torch
+import torch.distributed as dist
+from torch.cuda.streams import ExternalStream
+
+from sglang.srt.distributed.parallel_state import set_pdmux_status
+from sglang.srt.model_executor.forward_batch_info import ForwardMode
+from sglang.srt.multiplex.pdmux_context import (
+    get_current_stream_idx,
+    get_sm_counts,
+    get_stream_groups,
+    initialize_stream_groups,
+    load_pdmux_config,
+    set_current_stream_idx,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class SchedulerMultiplexMixin:
+
+    def init_pdmux(self):
+        # for pd_multiplexing, Init stream_groups, exclude normal stream for prefill only and decode only
+        self.pdmux_config = load_pdmux_config(self.server_args.pdmux_config_path)
+        initialize_stream_groups(self.gpu_id, self.pdmux_config)
+        self.stream_groups = get_stream_groups()
+        self.sm_counts = get_sm_counts()
+        self.real_sm_group_num = len(self.stream_groups)
+        logger.info(
+            f"PD-Multiplexing enabled with {self.real_sm_group_num} stream groups, sm_counts (prefill_sm, decode_sm): {self.sm_counts}"
+        )
+
+    # TODO(jason-fxz): This is a temporary demo
+    def adjust_stream_groups(self) -> tuple[int, tuple[ExternalStream, ExternalStream]]:
+        if not self.running_batch.is_empty() and self.split_prefill_batch:
+            decode_bs = self.running_batch.batch_size()
+            manual_divisions = self.pdmux_config.manual_divisions
+            if manual_divisions:
+                for i in range(len(manual_divisions)):
+                    _, _, threshold = manual_divisions[i]
+                    if decode_bs >= threshold:
+                        stream_idx = i + 1
+            else:
+                stream_idx = max(
+                    1,
+                    min(
+                        self.real_sm_group_num - 2,
+                        decode_bs
+                        * (self.real_sm_group_num - 2)
+                        // self.pdmux_config.decode_bs_divisor,
+                    ),
+                )
+            set_current_stream_idx(stream_idx)
+        elif not self.running_batch.is_empty():
+            set_current_stream_idx(self.real_sm_group_num - 1)
+        else:
+            set_current_stream_idx(0)
+
+        stream_idx = get_current_stream_idx()
+
+        self.tp_worker.model_runner.update_decode_attn_backend(stream_idx)
+        return stream_idx, self.stream_groups[stream_idx]
+
+    def update_split_prefill_batch(self, sm_count: int) -> bool:
+        if self.split_prefill_batch:
+            return False
+
+        # add new request
+        batch = self.get_new_batch_prefill()
+        if batch and not batch.is_empty():
+            batch.forward_mode = (
+                ForwardMode.SPLIT_PREFILL
+            )  # Set forward mode for split prefill
+            self.split_prefill_batch = batch
+            return True
+        return False
+
+    @torch.inference_mode()
+    def event_loop_pdmux(self):
+        """A scheduler loop for pd multiplexing."""
+        decode_done = False
+        prefill_done = False
+        wait_prefill_kernel_done = False
+        adjust_stream_group = False
+        stream_idx = get_current_stream_idx()
+        stream_group = self.stream_groups[stream_idx]
+        prefill_stream = stream_group[0]
+        decode_stream = stream_group[1]
+        torch.cuda.empty_cache()
+
+        logger.debug("Starting event loop for pd multiplexing...")
+
+        while True:
+            with torch.cuda.stream(decode_stream):
+                set_pdmux_status(False)
+                recv_reqs = self.recv_requests()
+                self.process_input_requests(recv_reqs)
+
+            with torch.cuda.stream(prefill_stream):
+                set_pdmux_status(True)
+                sm_count = self.sm_counts[stream_idx][0]
+                if not wait_prefill_kernel_done:
+                    adjust_stream_group = (
+                        self.update_split_prefill_batch(sm_count) or adjust_stream_group
+                    )
+
+            with torch.cuda.stream(decode_stream):
+                set_pdmux_status(False)
+                self.running_batch = self.update_running_batch(self.running_batch)
+                adjust_stream_group = adjust_stream_group or (
+                    stream_idx > 0 and self.running_batch.is_empty()
+                )
+                if self.running_batch.is_empty() and self.split_prefill_batch is None:
+                    self.check_memory()
+                    self.check_tree_cache()
+                    self.new_token_ratio = self.init_new_token_ratio
+                    self.maybe_sleep_on_idle()
+
+            if adjust_stream_group:
+                prefill_stream.synchronize()
+                decode_stream.synchronize()
+                stream_idx, stream_group = self.adjust_stream_groups()
+                prefill_stream = stream_group[0]
+                decode_stream = stream_group[1]
+                adjust_stream_group = False
+                logger.debug(
+                    f"Adjusting stream groups: {stream_idx}, prefill sm: {self.sm_counts[stream_idx][0]}, decode sm: {self.sm_counts[stream_idx][1]}"
+                )
+
+            with torch.cuda.stream(decode_stream):
+                set_pdmux_status(False)
+                # process decode batch
+                if self.running_batch and not self.running_batch.is_empty():
+                    decode_result = self.run_batch(self.running_batch)
+                    decode_done = True
+                else:
+                    decode_done = False
+            with torch.cuda.stream(prefill_stream):
+                set_pdmux_status(True)
+                if (
+                    self.split_prefill_batch
+                    and not self.split_prefill_batch.is_empty()
+                    and not wait_prefill_kernel_done
+                ):
+                    prefill_done = True
+                    forward_count = (
+                        max(
+                            1,
+                            self.pdmux_config.split_forward_token_budget
+                            // self.split_prefill_batch.extend_num_tokens,
+                        )
+                        if self.split_prefill_batch.extend_num_tokens > 0
+                        else self.model_config.num_hidden_layers
+                    )
+                    next_split_index = min(
+                        self.split_prefill_batch.split_index + forward_count,
+                        self.model_config.num_hidden_layers,
+                    )
+                    forward_count = (
+                        next_split_index - self.split_prefill_batch.split_index
+                    )
+
+                    self.split_prefill_batch.split_forward_count = forward_count
+                    prefill_result = self.run_batch(self.split_prefill_batch)
+                    if next_split_index == self.model_config.num_hidden_layers:
+                        self.split_prefill_batch.split_prefill_finished = True
+                        prefill_exe_done = prefill_stream.record_event()
+                    self.split_prefill_batch.split_index = next_split_index
+
+                elif wait_prefill_kernel_done:
+                    prefill_done = True
+                else:
+                    prefill_done = False
+
+            with torch.cuda.stream(decode_stream):
+                set_pdmux_status(False)
+                decode_stream.synchronize()
+                if decode_done:
+                    self.process_batch_result(self.running_batch, decode_result)
+
+            with torch.cuda.stream(prefill_stream):
+                set_pdmux_status(True)
+                if prefill_done and self.split_prefill_batch.split_prefill_finished:
+                    wait_prefill_kernel_done = True
+                    prefill_exe_done_flag = prefill_exe_done.query()
+                    flags = (
+                        torch.ones(1, device="cpu", dtype=torch.int32)
+                        if prefill_exe_done_flag
+                        else torch.zeros(1, device="cpu", dtype=torch.int32)
+                    )
+
+                    self.tp_cpu_group.allreduce(flags, dist.ReduceOp.SUM).wait()
+                    if flags.item() == self.tp_size:
+                        self.process_batch_result(
+                            self.split_prefill_batch, prefill_result
+                        )
+                        if self.running_batch and not self.running_batch.is_empty():
+                            self.running_batch.merge_batch(self.split_prefill_batch)
+                        else:
+                            self.running_batch = self.split_prefill_batch
+
+                        self.split_prefill_batch = None
+                        wait_prefill_kernel_done = False
+                        adjust_stream_group = True

python/sglang/srt/multiplex/pdmux_context.py

+from dataclasses import dataclass, field
+from typing import List
+
+import torch
+import yaml
+
+STREAM_GROUPS = []
+SM_COUNTS = []
+SM_GROUP_NUM = 8  # Default number of SM groups
+CURRENT_STREAM_IDX = 0
+CURRENT_STREAM_GROUP = None
+
+
+@dataclass
+class PDMuxConfig:
+    sm_group_num: int = 8
+    manual_divisions: List[List[int]] = field(
+        default_factory=list
+    )  # [prefill_sm, decode_sm, decode_bs_threshold]
+    split_forward_token_budget: int = 65536
+    decode_bs_divisor: int = 36
+
+
+def load_pdmux_config(config_path: str) -> PDMuxConfig:
+    """Load pdmux configuration from YAML file into a dataclass."""
+    if not config_path:
+        return PDMuxConfig()
+
+    with open(config_path, "r") as f:
+        raw = yaml.safe_load(f)
+
+    if "sm_group_num" not in raw:
+        raise ValueError("Missing required field: sm_group_num")
+
+    if raw["sm_group_num"] < 3:
+        raise ValueError("sm_group_num must greater than 3")
+
+    manual_divisions = raw.get("manual_divisions", [])
+
+    expected = raw["sm_group_num"] - 2
+    if manual_divisions and len(manual_divisions) != expected:
+        raise ValueError(
+            f"manual_divisions must have {expected} entries, "
+            f"but got {len(manual_divisions)}"
+        )
+
+    return PDMuxConfig(
+        sm_group_num=raw["sm_group_num"],
+        manual_divisions=manual_divisions,
+        split_forward_token_budget=raw.get("split_forward_token_budget", 65536),
+        decode_bs_divisor=raw.get("decode_bs_divisor", 36),
+    )
+
+
+def get_arch_constraints(compute_capability):
+    major, minor = compute_capability
+    # green context constraints for different architectures
+    if major == 6:
+        return 1, 1  # min_per_part, multiple
+    elif major == 7:
+        return 2, 2
+    elif major == 8:
+        return 4, 2
+    elif major == 9 and minor >= 0:
+        return 8, 8
+    else:
+        raise ValueError(f"Unsupported compute capability: {major}.{minor}")
+
+
+def divide_sm(total_sms, compute_capability, groups):
+    """
+    :param total_sms: total sm count on a single GPU
+    :param compute_capability: (major, minor)
+    :return: SM partition group(prefill sm, decode sm)
+    """
+    min_per_part, multiple = get_arch_constraints(compute_capability)
+    possible_values = [
+        x
+        for x in range(min_per_part, total_sms - min_per_part + 1, multiple)
+        if x >= total_sms - x and total_sms - x >= 16
+    ]
+    if not possible_values:
+        raise ValueError(
+            f"No valid partitions found for total SMs {total_sms} "
+            f"with constraints (min per part: {min_per_part}, multiple: {multiple})"
+        )
+
+    if len(possible_values) >= groups:
+        step = max(1, len(possible_values) // groups)
+        selected_values = possible_values[::step][:groups]
+    else:
+        selected_values = possible_values
+
+    divisions = []
+    for part1 in selected_values:
+        part2 = total_sms - part1
+        divisions.append((part1, part2))
+
+    divisions.reverse()  # Reverse to have larger prefill SM first
+
+    return divisions
+
+
+def initialize_stream_groups(gpu_id: int, config: PDMuxConfig):
+    from sgl_kernel import spatial
+
+    global STREAM_GROUPS, SM_COUNTS, SM_GROUP_NUM, CURRENT_STREAM_IDX, CURRENT_STREAM_GROUP
+    # for pd_multiplexing, Init stream_groups
+    device = torch.cuda.current_device()
+    total_sm_count = spatial.get_sm_available(gpu_id)
+    # (prefill_sm_count, decode_sm_count)
+    if config.manual_divisions:
+        divisions = [
+            (prefill_sm, decode_sm)
+            for prefill_sm, decode_sm, _ in config.manual_divisions
+        ]
+    else:
+        divisions = divide_sm(
+            total_sm_count,
+            torch.cuda.get_device_capability(device),
+            config.sm_group_num - 2,
+        )
+
+    SM_COUNTS = []
+    SM_COUNTS.append((total_sm_count, 0))  # Normal stream for prefill
+    SM_COUNTS.extend(divisions)  # Add the divided SM counts
+    SM_COUNTS.append((0, total_sm_count))  # Normal stream for decode
+    STREAM_GROUPS = []
+    STREAM_GROUPS.append(
+        (torch.cuda.Stream(gpu_id), torch.cuda.Stream(gpu_id))
+    )  # Normal stream for prefill
+    for prefill_sm, decode_sm in divisions:
+        STREAM_GROUPS.append(
+            (spatial.create_greenctx_stream_by_value(prefill_sm, decode_sm, gpu_id))
+        )
+    STREAM_GROUPS.append(
+        (torch.cuda.Stream(gpu_id), torch.cuda.Stream(gpu_id))
+    )  # Normal stream for decode
+
+    CURRENT_STREAM_IDX = 0
+    CURRENT_STREAM_GROUP = STREAM_GROUPS[CURRENT_STREAM_IDX]
+
+
+def set_current_stream_idx(idx: int):
+    global CURRENT_STREAM_IDX, CURRENT_STREAM_GROUP
+    if idx < 0 or idx >= len(STREAM_GROUPS):
+        raise ValueError(f"Invalid stream index: {idx}")
+    CURRENT_STREAM_IDX = idx
+    CURRENT_STREAM_GROUP = STREAM_GROUPS[CURRENT_STREAM_IDX]
+
+
+def get_stream_groups() -> list[tuple[torch.cuda.Stream, torch.cuda.Stream]]:
+    """Get the stream groups."""
+    return STREAM_GROUPS
+
+
+def get_sm_counts() -> list[tuple[int, int]]:
+    """Get the SM counts."""
+    return SM_COUNTS
+
+
+def get_current_stream_idx() -> int:
+    """Get the current stream index."""
+    return CURRENT_STREAM_IDX